Instructions to use mkhalifa/flan-t5-large-gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkhalifa/flan-t5-large-gsm8k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mkhalifa/flan-t5-large-gsm8k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mkhalifa/flan-t5-large-gsm8k")
model = AutoModelForSeq2SeqLM.from_pretrained("mkhalifa/flan-t5-large-gsm8k")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mkhalifa/flan-t5-large-gsm8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mkhalifa/flan-t5-large-gsm8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkhalifa/flan-t5-large-gsm8k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mkhalifa/flan-t5-large-gsm8k

SGLang

How to use mkhalifa/flan-t5-large-gsm8k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mkhalifa/flan-t5-large-gsm8k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkhalifa/flan-t5-large-gsm8k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mkhalifa/flan-t5-large-gsm8k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkhalifa/flan-t5-large-gsm8k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mkhalifa/flan-t5-large-gsm8k with Docker Model Runner:
```
docker model run hf.co/mkhalifa/flan-t5-large-gsm8k
```

GRACE: Discriminator-Guided Chain-of-Thought Reasoning

This model is a fine-tuned version of google/flan-t5-large on the GSM8K dataset. It serves as the generator for the GRACE (Guiding chain-of-thought ReAsoning with a CorrectnEss Discriminator) decoding strategy.

Model Description

GRACE is a stepwise decoding approach that steers the decoding process towards producing correct reasoning steps. It employs a step-level verifier or discriminator trained with a contrastive loss over correct and incorrect steps, which is used during decoding to score next-step candidates. This specific checkpoint is the fine-tuned generator model used in the paper's experiments for tasks like GSM8K.

Paper: GRACE: Discriminator-Guided Chain-of-Thought Reasoning
Repository: https://github.com/mukhal/grace

Citation

If you use this model or the GRACE approach, please consider citing the following paper:

@article{khalifa2023grace,
  title={Grace: Discriminator-guided chain-of-thought reasoning},
  author={Khalifa, Muhammad and Logeswaran, Lajanugen and Lee, Moontae and Lee, Honglak and Wang, Lu},
  journal={arXiv preprint arXiv:2305.14934},
  year={2023}
}

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Paper for mkhalifa/flan-t5-large-gsm8k

Discriminator-Guided Multi-step Reasoning with Language Models

Paper • 2305.14934 • Published May 24, 2023 • 1